基于矩阵特征值的主用户信号全盲检测算法

在多天线主用户信号检测过程中，在信道空闲和信道被占用2种情况下接收信号取样协方差矩阵的最大和最小特征值存在明显差异.根据这一观察，提出了一种新的基于取样协方差矩阵最大和最小特征值的盲检测算法.该算法以取样协方差矩阵最大与最小特征值的差与和的比值作为感知判决量，再通过引入大维随机矩阵中关于取样协方差矩阵最大和最小特征值分布的最新成果，设计出一种有效的判决门限计算方法.相对于经典的特征值检测算法，蒙特卡罗仿真实验比对结果表明，新算法具有感知判决门限计算准确的优点，能有效地提高检测性能和判决结果的可靠性.

Blind Primary User Signal Detection Algorithm Based on Matrix Eigenvalues

In the process of multi-antenna primary user signal detection, the maximum eigenvalue and the minimum eigenvalue of the sampling covariance matrix of the received signal are significantly different when the channel is idle and the channel is occupied. According to this observation, a new blind detection algorithm based on the eigenvalues of sampling covariance matrix is proposed. The ratio of the difference and sum of the maximum eigenvalue to the minimum eigenvalue of the sampling covariance matrix is used as the decision statistic. By introducing the latest results on the distribution of maximum eigenvalues and minimum eigenvalues of the sampled covariance matrices in the large-dimensional random matrix theory, an effective method for calculating decision threshold is proposed. Compared with the classical eigenvalue detection algorithm, the new algorithm has the advantage of accurate calculation of the decision threshold, and effectively improves the detection performance and the reliability of decision results. The feasibility and superiority of the new algorithm are verified by Monte Carlo simulation experiments.